{"id":"W4408283730","doi":"10.1038/s41598-025-90169-y","title":"3D volumetric tomography of clouds using machine learning for climate analysis","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Atmospheric aerosols and clouds","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Theoretical Astrophysics; University of Toronto","funders":"European Research Council","keywords":"Computer science; Rendering (computer graphics); Remote sensing; Radiation; Cloud computing; Tomography; Meteorology; Environmental science; Geology; Artificial intelligence; Geography; Physics; Optics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003018251,0.0004441091,0.0003907279,0.0007540355,0.0002294872,0.000632939,0.0004823936,0.0005084744,0.0007557707],"category_scores_gemma":[0.00140153,0.0002674476,0.0004993487,0.001110607,0.0004170133,0.0006830445,0.0007091545,0.0006845183,0.0002278548],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000507025,"about_ca_system_score_gemma":0.0004634113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002956203,"about_ca_topic_score_gemma":0.004140135,"domain_scores_codex":[0.9998535,0.00003771189,0.000007406609,0.00002705505,0.00005529207,0.00001902743],"domain_scores_gemma":[0.9995882,0.0002251223,0.00006948948,0.00004785841,0.00005103205,0.00001834389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000452458,0.00005395573,0.00389687,0.0000732128,0.00004601323,0.00005890195,0.00004963392,0.8950627,0.01255633,0.005241634,0.0008026182,0.08211281],"study_design_scores_gemma":[0.000001046225,0.000002705118,0.0003402288,0.000002736353,0.000001096036,0.000006565083,0.000004086652,0.9967467,0.0008913845,0.001800219,0.0001994064,0.000003853882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06977449,0.0007174581,0.9254088,0.0003862544,0.00005413574,0.00003858829,0.0004594662,0.0009897097,0.002171061],"genre_scores_gemma":[0.7641064,0.0005601415,0.2338463,0.0001084155,0.00005978967,0.00008214748,0.000588857,0.00008838868,0.0005596274],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002956203,"threshold_uncertainty_score":0.005878031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008819883123711831,"score_gpt":0.2471656514872166,"score_spread":0.2383457683635048,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}